Neuro-inspired computation has the potential for becoming the next revolution in computational systems, opening possibilities for transcending the limitations of von Neumann and Turing architectures and of Moore's Law. Neural networks and other neuro-inspired computational approaches have long been a subject of research. However, many of these approaches have been realized only in simulations implemented on digital computers. Relatively few have been implemented directly on hardware platforms specifically designed for neuro-inspired operations. Of those specialized hardware platforms that have been implemented, some are scalable only to a limited degree because spatial constraints limit the number of electrical interconnections that can be made between individual unit cells. Hence there remains a need for further approaches to neuro-inspired computation and for new architectures for platforms to support such approaches.